Occupancy pattern detection, estimation and prediction

ABSTRACT

Systems and methods are described for predicting and/or detecting occupancy of an enclosure, such as a dwelling or other building, which can be used for a number of applications. An a priori stochastic model of occupancy patterns based on information of the enclosure and/or the expected occupants of the enclosure is used to pre-seed an occupancy prediction engine. Along with data from an occupancy sensor, the occupancy prediction engine predicts future occupancy of the enclosure. Various systems and methods for detecting occupancy of an enclosure, such as a dwelling, are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.13/936,028, filed on Jul. 5, 2013, now allowed, which is a continuationof U.S. application Ser. No. 12/881,430, filed on Sep. 14, 2010, nowU.S. Pat. No. 8,510,255. Each of the above-referenced patentapplications is incorporated by reference herein.

COPYRIGHT AUTHORIZATION

A portion of the disclosure of this patent document may contain materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND AND SUMMARY

This invention generally relates to occupancy detection, estimation andprediction. More particularly, embodiments of this invention relate topredicting occupancy of an enclosure and to systems and methods fordetecting occupancy of an enclosure.

Techniques for detecting or sensing occupancy in a structure such as abuilding is known for a number of applications. For example, anoccupancy sensor device attempts to determine if someone is in a room,and is often used in home automation and security systems. Manyoccupancy sensors that are used for home automation or security systemsare based on motion sensors. Motion sensors can be mechanical, forexample a simple tripwire, or electronic. Known methods for electronicoccupancy detection include acoustical detection and optical detection(including infrared light, visible, laser and radar technology). Motiondetectors can process motion-sensor data, or employ cameras connected toa computer which stores and manages captured images to be viewed andanalyzed later or viewed over a computer network. Examples of motiondetection and sensing applications are (a) detection of unauthorizedentry, (b) detection of cessation of occupancy of an area to extinguishlighting and (c) detection of a moving object which triggers a camera torecord subsequent events. A motion sensor/detector is thus important forelectronic security systems, as well as preventing the wastefulillumination of unoccupied spaces.

Furthermore, some applications can greatly benefit from (even modestlyaccurate) predictions of future occupancy. For example, heating orcooling of a structure to an acceptable temperature range has anassociated lag time of several minutes to more than one hour betweenactuation and achieving the desired thermal conditions. Therefore it isbeneficial to predict with some statistical accuracy, ahead of time,when an occupant or occupants will be entering and/or leaving structure,or moving between different regions or rooms within the structure.

Attempts have been made at predicting occupancy. For example, M. Mozer,L. Vidmar, and R. Dodier, “The Neurothermostat: Predictive OptimalControl of Residential Heating Systems” appearing in M. Mozer et al.Adv. in Neural Info. Proc. Systems 9, (pp. 953-959). Cambridge, Mass.:MIT Press. 1997, discusses a research project in which an occupancypredictor uses a hybrid neural net/look-up table to estimate theprobability that an occupant will be home.

According to some embodiments, systems and methods for predictingoccupancy of an enclosure are provided. The systems can include a modelof occupancy patterns based in part on information of the enclosureand/or the expected occupants of the enclosure, and a sensor adapted todetect occupancy within the enclosure. An occupancy predictor is adaptedand programmed to predict future occupancy of the enclosure based atleast in part on the model and the occupancy sensor. The model ispreferably an a priori stochastic model of human occupancy created priorto installation of the system into the enclosure, and the modelpreferably includes behavior modeling of activity, itinerary, and/orthermal behavior.

According to some embodiments, the model is based at least in part onthe type of the enclosure, with exemplary types including: workplace,single-family home, apartment, and condominium. According to someembodiments, the model is based at least in part on geometrical andstructural data about the enclosure.

According to some embodiments, the model is based at least in part on anexpected type of occupant of the enclosure. Examples of types ofoccupant attributes include: age, school enrollment status, maritalstatus, relationships status with other occupants, and retirementstatus. Examples of expected occupant types include: preschool children,school-age children, seniors, retirees, working-age adults, non-coupledadults, vacationers, office workers, retail store occupants.

According to some embodiments, the model is based in part on seasons ofthe year and/or the geographic location of the enclosure. The enclosurecan be various types of dwellings and/or workplaces.

According to some embodiments, the occupancy prediction of the enclosureis also based in part on user-inputted data, such as occupancyinformation directly inputted by an occupant of the enclosure, and/orcalendar information such as holidays, seasons, weekdays, and weekends.

The occupancy prediction can be used in the actuation and/or control ofan HVAC system for the enclosure or various other applications such as:home automation, home security, lighting control, and/or the charging ofrechargeable batteries.

According to some embodiments, various systems and methods for detectingoccupancy of an enclosure, such as a dwelling, are provided. Examplesinclude: detecting motion, monitoring communication signals such asnetwork traffic and/or mobile phone traffic, monitoring sound pressureinformation such as in the audible and/or ultrasonic ranges, monitoringutility information such powerline information or information from SmartMeters, monitoring motion in close proximity to the sensor, monitoringinfrared signals that tend to indicate operation of infraredcontrollable devices, sudden changes in ambient light, and monitoringindoor air pressure (to distinguish from pressure mats used in securityapplications) information which tends to indicate occupancy.

According to some embodiments, the occupancy predictor includes one ormore algorithms for predicting occupancy based on one or more occupancypatterns, and the occupancy predictions are based in part on amaximum-likelihood approach.

As used herein the term “model” refers generally to a description orrepresentation of a system. The description or representation can usemathematical language, such as in the case of mathematical models.Examples of types of models and/or characteristics of models, withoutlimitation, include: lookup tables, linear, non-linear, deterministic,probabilistic, static, dynamic, and models having lumped parametersand/or distributed parameters.

As used herein the term “sensor” refers generally to a device or systemthat measures and/or registers a substance, physical phenomenon and/orphysical quantity. The sensor may convert a measurement into a signal,which can be interpreted by an observer, instrument and/or system. Asensor can be implemented as a special purpose device and/or can beimplemented as software running on a general-purpose computer system.

It will be appreciated that these systems and methods are novel, as areapplications thereof and many of the components, systems, methods andalgorithms employed and included therein. It should be appreciated thatembodiments of the presently described inventive body of work can beimplemented in numerous ways, including as processes, apparata, systems,devices, methods, computer readable media, computational algorithms,embedded or distributed software and/or as a combination thereof.Several illustrative embodiments are described below.

BRIEF DESCRIPTION OF THE DRAWINGS

The inventive body of work will be readily understood by referring tothe following detailed description in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a block diagram illustrating a system for occupancyprediction, according to some embodiments;

FIG. 2 is a block diagram illustrating examples of statistical profilesfor predicting occupancy, according to some embodiments;

FIG. 3 is a block diagram illustrating occupancy prediction based inpart on scheduling inputs, according to some embodiments;

FIG. 4 is a block diagram illustrating occupancy detection based onsensing, according to some embodiments;

FIG. 5 is a diagram of a structure in which occupancy is predictedand/or detected, according to various embodiments;

FIG. 6 is a diagram of an HVAC system, according to some embodiments;and

FIG. 7 is a schematic of a processing system used to predict and/ordetect occupancy of an enclosure, according to some embodiments.

DETAILED DESCRIPTION

A detailed description of the inventive body of work is provided below.While several embodiments are described, it should be understood thatthe inventive body of work is not limited to any one embodiment, butinstead encompasses numerous alternatives, modifications, andequivalents. In addition, while numerous specific details are set forthin the following description in order to provide a thoroughunderstanding of the inventive body of work, some embodiments can bepracticed without some or all of these details. Moreover, for thepurpose of clarity, certain technical material that is known in therelated art has not been described in detail in order to avoidunnecessarily obscuring the inventive body of work.

According to some embodiments, methods and systems for estimating,predicting into the future and transmitting specific data or statisticsof occupancy or occupancy patterns in a structure such as a home,apartment, other dwelling or building are described. Predicting and/ordetecting occupancy of an enclosure, such as a building, can be used fora number of applications. For example, applications that can benefitfrom accurate occupancy prediction include heating, ventilating and airconditioning (HVAC), lighting management, hot water heating andmanagement, security, emergency response, and the management andcharging of rechargeable batteries (e.g. for electric vehicles). Ingeneral, applications that greatly benefit from occupancy prediction arethose that particularly benefit from knowing or accurately estimating,in advance, when the structure will be occupied. The lead-time of theprediction will especially aid applications that have an inherentlag-time to reach a certain state. For example, heating and cooling astructure to an acceptable level has an associated lag time of severalminutes to more than one hour. Therefore it is beneficial to accuratelypredict ahead of time, when an occupant or occupants will be enteringand/or leaving structure. Additionally, energy savings can be obtaineddue to predicting and/or detecting occupancy for both short term, suchas intraday periods and long term, such as multi-day vacation periods,when the structure can remain unconditioned or more economicallyconditioned.

According to some embodiments, systems and methods for detecting,estimating and predicting into the future occupancy patterns within astructure are described. The systems can include a prior (a priori)stochastic model of human occupancy, thermal comfort and activitypatterns, based in part on information pertaining to the type,dimensions, layout and/or the expected average number of occupants ofthe structure (whether a home or other type of structure) and on thecalendar (time of year, day of week, time of day), and also based onprevailing and forecast local weather conditions. Such a stochasticmodel can have multiple parameters, which can be initially estimatedfrom a questionnaire filled by residents and/or from accumulatedstatistical data for structures of type and usage, and occupantcharacteristics (i.e. according to household type) similar to thestructure in question. Over time, the parameters of the a prioristochastic occupancy/comfort/activity model, can be further trainedusing cumulative logs of sensor data acquired within the actualstructure in question. This can be carried out by comparing the a priorimodel to the actual output of the occupancy prediction engine 120. Forexample, if the a priori model predicts the absence of occupants onWednesdays during daytime, but occupancy sensors sense human presence onWednesdays consistently for several weeks, the a priori behavior modelcan be corrected for this information. Similarly, aggregates of outputfrom the prediction engine 120 can be used to refine other parameters ofthe a priori behavior model. The method for carrying out such updatescan use the some or a combination of the algorithms mentioned herein forthe prediction engine itself. These corrected a priori models if storedin a database can inform future a priori models to pre-seed other homeswith similar structures or households.

According to some embodiments, the system can also include one orseveral occupancy sensor(s) and associated software, adapted to detectoccupancy within the structure or within particular regions of thestructure. The system and methods can include occupancy-patternestimation and prediction algorithms, the computations of which may berun locally and/or remotely on an internet server. The estimation andprediction algorithms can utilize the a priori stochastic model,together with local motion and occupancy sensor/detector data andweather reports/forecasts, to compute maximal-likelihood estimators forthe current, past and near-term future human occupancy patterns withinthe structure, even in regions or rooms within the structure which areoutside the range of the motion sensor devices installed in thestructure.

According to some embodiments, systems and methods for detecting,estimating and predicting into the future occupancy patterns within astructure are described, in which the occupancy-pattern predictor isadapted and programmed to predict statistically likely patterns offuture occupancy in the structure based at least in part on the prior (apriori) stochastic occupancy/comfort/activity model, and also on theoccupancy and motion sensors and detectors. The a priori model ispreferably created prior to installation of the system into thestructure, and it preferably stochastically models human motions withinthe structure; exits and entrances from and into the structure;itineraries of activity types; and human thermodynamic comfort in termsof ambient conditions such as temperature, humidity, airflow, motion,clothing and interaction with furniture. The a priori stochastic modelcan include one or more statistical profiles.

According to some embodiments, systems and methods for detecting,estimating and predicting into the future occupancy patterns within astructure are provided, in which a subset of the occupancy-patternestimator and predictor algorithms embody analytical and numericalmethods to approximate, and rapidly compute, the past and future timeevolution of the spatial distribution of thermodynamic environmentalstate variables within the structure. The list of thermodynamic statevariables can include ambient temperature and humidity. In someembodiments, it may also include particulate-matter densities.

According to some embodiments, the a priori stochastic model is based atleast in part on the type of structure in question, with exemplary typesincluding but not limited to: workplace, single-family home, apartment,and condominium.

According to some embodiments, the a priori stochastic model is based atleast in part on an expected distribution of types of occupantsresiding, or frequenting, the structure, and of their attributes.Examples of occupant attributes include: age, school enrollment status,marital status, relationships status with other occupants, andretirement status. Examples of expected occupant types include:preschool children, school-age children, seniors, retirees, working-ageadults, non-coupled adults, adults comprising a family, vacationers,office workers, retail store occupants.

According to some embodiments, the a priori stochastic model is based inpart on seasons of the year and/or the geographic location of thestructure. The structure can be one of various types of dwellings and/orworkplaces.

According to some embodiments, the occupancy-pattern estimations andpredictions for the structure can be based in part on user-inputteddata, such as occupancy-questionnaire and temperature/humidity/fan-speedset point preferences, which are from time to time directly inputted byoccupants of the structure. The estimation and prediction computationscan also be based in part on calendar information such as holidays,seasons, weekdays, and weekends. Estimation and prediction computationscan be performed on various timescales. Estimations and predictions ofoccupancy patterns within a given time window on a given day (whetherpast, present or future) can give special weight to past occupancyand/or occupant-supplied data logged during the same time of day and/orthe same day of the week.

FIG. 1 is a block diagram illustrating a system for occupancyprediction, according to some embodiments. In FIG. 1, the occupancyprediction engine 120 can be pre-seeded or pre-loaded with occupancypatterns 114 such as behavior models. Based on the pre-loaded behaviormodels 114, which can be combined 118 and inputted, the predictionengine 120 makes predictions of occupancy. The prediction enginecompares its predictions with actual occupancy sensor data, for example,from occupancy sensors 110, and/or from direct input from a user viainput 130. If the prediction is incorrect, according to someembodiments, the occupancy prediction engine 120 learns so as to avoidmaking incorrect predictions in future situations having similarcircumstances.

According to some embodiments, occupancy prediction engine 120 providesa probability distribution on when the inhabitants are expected todepart and arrive. According to some embodiments a method found to beuseful when data quality is not always of high quality, is to make a“seasonal model.” According to the seasonal model, it is known that aday is 24 hours long, and there are separate cases for workdays andnon-workdays. According to some embodiments, there can be separate casesfor all days of the week, additionally, for certain seasonal periodssuch as summer and winter.

According to some embodiments a probability distribution (for example,the normal distribution, or some other distribution) is fitted to thedetected or directly sensed departure and arrival times of theinhabitants′. Such a process may be applicable in cases of relativelypoor occupancy data quality, for example, where assumptions have to bemade about the inhabitants' habits such as that they're only out of thehouse one time per day.

Slightly more complex techniques may be suitable when improved dataquality is available. According to some embodiments, a seasonal ARMA(autoregressive moving average) model is fitted to the occupancy data.According to such embodiments, it is assumed that the occupancy data hasa certain periodic component (for example, the “season”) but alsodepends on the previous observations in a certain way. This would allowa prediction of multiple leave/arrive events in a single day—providedthat the available data is good enough to support it.

According to some embodiments, occupancy predictions are obtaineddirectly from the data stream of the sensors. For example, the detectorssuch as occupancy sensors 110 have some level coming out of it, that isnormally rounded to ‘1’ for ‘occupied’ or ‘0’ for ‘unoccupied’ in thecombine and filters system 112. However, in these direct embodiments,the output from occupancy sensors 110 are kept between 0 and 1 for usein a more complex model. There are many types of models that could beused. For example, a graphical model, which is somewhat akin to a neuralnet, could be used.

According to some embodiments, AR (auto regressive), MA (movingaverage), or ARMA (autoregressive moving average) time series methodsare used. According to some embodiments an automated regression withexogenous terms (ARX) is used.

According to some other embodiments, the parameters of arrival/departuretime parametric distribution, such as normal, gamma, beta, poisson, orany other distribution, are estimated from the occupancy data.

According to some other embodiments, a Graphical Model includingBayesian Network, Hidden Markov Model, Hidden Semi-Markov Model, othervariant of Markov model, Partially Observable Markov Decision Processmodels can be used for occupancy prediction.

According to other embodiments, one or more of the following could beused in prediction engine 120 alone or in combination: Neural Network,Latent Variable Model, Bayesian Network, Graphical Model, RegressionTree. other tree-based model, Additive Models, (Linear, Quadratic,Fisher, or another) Discriminant Analysis, Support Vector Machines,another classification technique, Principle Component Analysis, FactorAnalysis, Kernel Methods, Ordinary Regression, Logistic Regression,Penalized (or Regularized) Regression, another type of regression,boosting methods, Splines, Wavelets, Least-squares function fitting,another type of function estimation, Expectation-Maximization Algorithm,Belief Propagation Algorithm, Nearest Neighbor Algorithms, ClusteringAlgorithms, another learning technique, another time series forecastingtechnique.

According to some embodiments, the modeling technique used for occupancyprediction engine 120 is to gather inputs, e.g. combined and filteredoccupancy sensor data 110, combined occupancy patterns 114, directoccupancy inputs 130, inputted user data 132, and occupant Q & A 136.Weights or other statistical methods are then assigned or attributed toeach input. The weighted inputs are then totaled in real time to get theoccupancy prediction. When designing the weight for each input, ananalysis of each data type should be undertaken. For example, whenincorporating data from occupancy sensors 110 and/or from a user viainput 130, the direct user input 130 may be assumed to have no error,while the sensors 110, may be assumed to have some error depending onthe type of sensor.

According to some embodiments, the prediction engine 120 looks fordifferent periodicities such as daily, weekly, monthly, seasonally, andyearly, in the inputs from some or all of patterns 114, occupancy inputs130, calendar data 132 and sensor data 110. According to someembodiments, the events and/or patterns from the near past can be moreheavily weighted than events and/or patterns from the more distant past.

According to some embodiments, systems and methods for occupancyprediction with increased accuracy are provided that include seeding anoccupancy prediction model in advance. Such pre-seeding can be doneusing one or more types of information. For example, pre-seeding of anoccupancy prediction model can be performed depending on a group type,where the group can be demographic, location, geographic area. Increasedaccuracy of occupancy prediction can be obtained by pre-seeding theoccupancy prediction models with data based on prior information,followed by using occupancy sensing data to modify the model accordingto the sensor data.

According to some embodiments, prediction engine 120 is pre-seeded byusing appropriate values for various coefficients in mathematical modelsprior to the models entering the learning phase via sensor data input.By pre-seeding, the prediction engine 120 can already have a goodstarting point prior to any sensing input. The learning time when placedinto a new environment also can be reduced.

According to some other embodiments a neural net or look up table can beused as part of the prediction engine 120. For further detail, see: M.Mozer, L. Vidmar, and R. Dodier, “The Neurothermostat: PredictiveOptimal Control of Residential Heating Systems” appearing in M. Mozer etal. Adv. in Neural Info. Proc. Systems 9, (pp. 953-959). Cambridge,Mass.: MIT Press. 1997, which is incorporated by reference herein.

According to some embodiments, examples of statistical profiles used foroccupancy prediction will be provided. FIG. 2 is a block diagramillustrating examples of statistical profiles for predicting occupancy,according to some embodiments. Occupancy patterns 114 in the form ofstatistical profiles can be based on several types of information, forexample, based on structure types, for occupant type data, and forseasons and locations and can be informed at least in part by actualsurvey data.

Profiles 210 are examples of statistical profiles for predictingoccupancy based on structure type data. For example, statisticalprofiles can be used for structure types such as single-family homes,apartments, condominiums, offices, etc. For workplaces, for example,likelihoods of scheduled periods of occupancy and non-occupancy can beused to predict occupancy. Company and individual worker scheduleinputs, if available, can also be used. Holiday schedule inputs, ifavailable, can also be used.

If the structure type is a single-family home, for example, thenlikelihoods of varied awake hour occupancy patterns throughout thedwelling can be used to predict occupancy. Likelihoods of sleep houroccupancy patterns confined regularly to certain rooms and zones canalso be used.

If the structure type is an apartments or condominiums, then likelihoodsof living areas close together can be used to predict occupancy. Anincreased likelihood of regular local patterns of occupancy be used; ascan an increased likelihood of full use of the space during occupiedwaking hours.

Profiles 220 are examples of statistical profiles for predictionoccupancy based on occupant type. Such occupant types include:roommates, families, seniors, and type of company or business. Forexample, dwellings with one or more preschool age children can reflectthe following likelihoods: (1) few regularly scheduled 4+ hour periodswhen the home isn't occupied; (2) few regular 4+ hour night periods whenevery occupant is asleep; and (3) that one or more occupants will bothretire and rise relatively early each day.

In another example, dwellings with one or more school age children canreflect the following likelihoods: (1) the home being regularlyunoccupied for 4+ hours; (2) occupants having varied occupancyschedules; (3) the need for school calendar and vacation data inputs;(4) the need for work calendar and vacation data inputs; and (5) ifteenagers, that one or more occupant will retire relatively late eachday

In another example, dwellings exclusively of seniors or retirees canreflect the following likelihoods: (1) few regularly scheduled 4+ hourperiods when the home isn't occupied; (2) the need for activity scheduleinputs; and (3) that occupants will retire early and rise late, and maysleep during the day.

In another example, dwellings exclusively of coupled working age adultscan reflect the following likelihoods: (1) occupants having similar awayschedules and sleep schedules; (2) occupants gathering regularly in thedwelling; and (3) the need for work and/or school calendar and vacationdata inputs

In another example, dwellings exclusively of non-coupled working ageadults can reflect the following likelihoods: (1) occupants havingvaried away schedules and sleep schedules; (2) occupants separatingregularly in the dwelling; and (3) the need for work and/or schoolcalendar and vacation data inputs.

In another example, dwellings that are vacation homes can reflect alikelihood of long periods of non-occupancy.

In another example, places of work, including offices and retail storescan reflect the following likelihoods: (1) scheduled periods ofoccupancy and non-occupancy; (2) the need for company and individualwork schedule inputs; (3) the need for work holiday schedule inputs; and(4) store opening hours.

Profiles 230 are examples of statistical profiles for predictionoccupancy based on seasons and locations. In particular, according tosome embodiments, information about weather and climate based onlocation information is combined with the behavior model informationdescribed above.

For example, dwellings in locations experiencing very cold or very hotseasonal weather or seasonal precipitation can reflect likelihoods ofincreased occupancy. In another example, dwellings in locationsexperiencing temperate seasonal weather can reflect likelihoods ofdecreased occupancy. In another example, dwellings in locations whichseasonally experience temperate weather during the day but very coldweather at night can reflect likelihoods of decreased daily butincreased evening occupancy. An in another example, dwellings inlocations which seasonally experience very hot weather during the daybut temperate weather at night can reflect likelihoods of increaseddaily but decreased evening occupancy.

According to some embodiments behavior models are predeterminedschedules that are used to give initial conditions to the predictionengine 120. It can be thought of as additional statistical data this isinput to engine 120 in a similar fashion as is data that is collectedfrom other inputs such as from sensors 110.

FIG. 3 is a block diagram illustrating occupancy prediction based inpart on scheduling inputs, according to some embodiments. Profiles 132are examples of statistical profiles for prediction occupancy based onscheduling inputs. According to some embodiments, regular schedulingdata is input (at the thermostat and/or remotely) that defines, forexample: regular periods daily, weekly, monthly or yearly when thedwelling will be vacant; regular periods daily, weekly, monthly oryearly when the dwelling will be occupied; and regular periods dailywhen the occupants will be asleep.

According to some embodiments, one-time scheduling data is input (at thethermostat and/or remotely) that deviates from regular occupancypatterns, that defines, for example: sustained periods of vacancy (suchas a vacation or day away); and sustained periods of occupancy (such asbeing home recovering from illness or taking a day off from work).

According to some embodiments, real-time scheduling data is input (atthe thermostat and/or remotely) that defines, for example: occupantsreturning early from scheduled activities; occupants leavingunexpectedly; and or other changes in the usual schedule.

According to some embodiments, calendar data from local school schedulesare input that reflect a likelihood of increased occupancy for familiesfor days when school is not in session (such as snow days, half-days, orgovernment days).

According to some embodiments, calendar data from local governmentschedules are input, that reflect a likelihood of increased occupancyfor holidays and national or local days off.

According to some embodiments, personal calendar data gathered from theinternet, smart phones, or PDAs are input that reflect likelihoods: ofdecreased occupancy during travel, events, appointments, and meetings;of increased occupancy during events hosted in the dwelling; ofincreased occupancy in the absence of scheduled events elsewhere.

According to some embodiments, a 365 day calendar for occupancyscheduling and prediction is input that reflects the likelihood ofyearly and seasonal events leading to periods of increase or decrease inoccupancy; for example around December 25, many families experienceincreased occupancy at home and many places of work, decreasedoccupancy.

According to some embodiments, further detail on systems and methods fordetecting occupancy will not be provided. FIG. 4 is a block diagramillustrating occupancy detection based on sensing, according to someembodiments. One or more of the shown sensing techniques can be used todetect occupancy.

According to technique 410, background noise on the main powerline ismonitored and filtered to detect the use of electronic devices, whichindicates a likelihood of occupancy in the dwelling.

According to technique 412, network connections (wifi, email or specificports) are monitored for changes in traffic, which indicates alikelihood of internet usage and therefore occupancy. According to someembodiments physical layer traffic can be monitored for patternsindicating occupancy. For example, radio signals can be detected usingan antenna. According to some other embodiments, packets or traffic inany of the software layers (such as monitoring ports, or just lookingfor more traffic in general) can be sniffed.

According to technique 414, a radio sensor is used monitor changes inlow emission radio waves reflecting the likely presence of occupants.This technique is similar to the network traffic monitoring techniquedescribed above, but applied to monitoring of mobile phone traffic orother radio signals. According to some embodiments, the two techniques412 and 414 are used in combination.

According to technique 416, a microphone is used to monitor sounds(within the audible range) that reflect likelihoods of occupancy, suchas footsteps, voices, doors closing.

According to technique 414, an infrared sensor is used to monitor theheat of the dwelling so that rapid changes in temperature within a closeproximity, e.g. ten feet, of the sensor (such as people moving), whichreflects a likelihood of occupancy.

According to technique 418, an infrared sensor is used to detect the useof infrared devices such as a television remote, which reflects alikelihood of occupancy. A majority of remotes use infrared technologyto send commands to the television, stereo, etc. This communication isnot direct in space but is spread around the room. The occupancy sensingdevice, such as a thermostat, can pick up these commands and infer thatpeople are home if they are using their remotes.

According to technique 420, an accelerometer is used to monitor changesin motion that reflect likelihoods of occupancy within 1 meter of thesensor. Accelerometers measure acceleration and therefore implied force.Using one of these sensors, vibrations due to footsteps or other humanmotion can be detected when occupants are nearby.

According to technique 422, an ultrasonic (sonar) microphone is used tomonitor sounds out of audible range that reflect likelihoods ofoccupancy. Activity such as footsteps and air moving due to humanmovement create noise in the ultrasonic range. The ultrasonic microphoneis used to pick such noise and infer based on pattern recognition andchanges from the baseline that humans are present. According to someembodiments, current temperature data is used to correct for changes inthe speed of sound due to temperature.

According to technique 426, a sensor is used to detect the motion of anoccupant towards the occupancy-sensing device, such as a thermostat,reflecting the likelihood that the occupant intends to interact with it.In anticipation, the interface is automatically prepared for useractivity. It has been found that such automatic preparation reduces anylag time perceived by the user as well as significantly enhance the userinterface experience. There are multiple sensing techniques that can beused for detecting proximity. According to some embodiments, an IRtransmitter and receiver are used to measure the bounce back of thetransmission in the receiver. If occupants are nearby, the signal comesback strongly and increasingly strong as they approach.

According to technique 428, smart utility meters, such as Smart Meters,are used to monitor energy consumption reflecting the likely presence ofoccupants. With real time (or near real time) energy usage information,changes in activity can be measured to imply occupancy, patternrecognition on changes from baseline are used to reflect likelihoods ofoccupancy. As used herein the term “Smart Meter” refers to any advancedutility meter that identifies consumption in greater detail than aconventional utility meter. Smart Meters generally, but not necessarilyhave the capability of communicating consumption information via somenetwork back to a local utility supply service for monitoring and/orbilling purposes. Smart Meters often refer to electrical meters, but canbe used to measure other utilities such as natural gas and water.

According to technique 432, ambient light sensing can indicate presenceof artificial light source and distinguish from natural light. Presenceof artificial lighting can indicate occupancy. Sudden changes in ambientlight can also indicate presence of occupants, such as with switching onand off lights or opening or closing of blinds and/or curtains.According to some embodiments the wavelength composition of incidentlight can be measured which can help distinguish whether the lightsource is artificial or natural.

According to technique 434, gas composition can be sensed or monitoredfor certain components that tend to indicate the presence of occupants.For example, a CO2 sensor can be used to detect levels of CO2 that tendto indicate the presence of occupants. According to some embodiments,sensors for volatile organic compounds can be used to detect pollutantsor other gaseous components that tend to indicate the presence ofoccupants.

According to some embodiments, known GPS and or cell-tower locatingtechnology for locating personal devices such as mobile phones can beused in combination with one or more of the other occupant sensingtechniques described herein to aid in occupancy detection.

According to technique 424, a pressure sensor is used to detect opendoors/windows. By using a sensitive pressure sensor, you can monitor thechange in atmospheric pressure in the home when doors or windows areopened, reflecting a likelihood of occupancy.

According to some embodiments, a combination of techniques 430 are usedto determining likelihoods of occupancy, depending on factors, such asthe type of structure. For example, a combination of near fielddetection methods with far detection methods is used for the mosteffective occupancy detection depending on the type of dwelling.Dwellings encompassing large spaces reflecting likelihoods of areaswithin the dwelling that cannot be reached by line of sight detectionmethods; so as to benefit from radar or other far field detectionmethods. Dwellings such as apartments that are limited spaces insidelarger buildings benefit from detection methods that are restricted tothe limited unit such as infrared or other line of sight methods. In anapartment, there are fewer places to walk by and the detection device,such as a thermostat, will be passed by more frequently (so near fieldsensing techniques such infrared is preferred). However, apartmentsoften have lots of close neighbors, so far field techniques (such asnetworked traffic monitoring and radio traffic monitoring) tend not towork as well because those techniques also detect occupancy of theneighbors. On the other hand, in a large single-family home, dwellersmay not pass by the thermostat as frequently as the apartment dweller,but because there are no close neighbors, far field techniques tend towork better.

FIG. 5 is a diagram of a structure in which occupancy is predictedand/or detected, according to various embodiments. Structure 500, inthis example is a single-family dwelling. According to otherembodiments, the structure can be, for example, a duplex, an apartmentwithin an apartment building, a commercial structure such as an officeor retail store, or a structure that is a combination of the above.Occupancy prediction device 510 is shown located within structure 500and includes all or some of the functionality described with respect toFIG. 1, including the occupancy prediction engine 120 and the occupancysensors 110. According to some embodiments, one or more separateoccupancy sensors, such as sensor 512 and 514 are located within thestructure 500. The occupancy sensors such as can be located withindevice 510 or separately, such as sensors 512 and 514, can be used tocarry out the techniques described with respect to FIG. 4. According tosome embodiments, output regarding occupancy from device 510 iscommunicated to a system 520. Examples of system 520 include an HVACsystem for the structure 500, a security system, a lighting controlsystem, and a high capacity battery charging system, such as could beused to power electric vehicles in garage 530.

FIG. 6 is a diagram of an HVAC system, according to some embodiments.HVAC system 620 provides heating, cooling, ventilation, and/or airhandling for the structure, such as a single-family home 500 depicted inFIG. 5. The system 620 depicts a forced air type system, althoughaccording to other embodiments, other types of systems could be usedsuch as hydronic and/or in-floor radiant heating. In heating, heatingcoils or elements 642 within air handler 640 provide a source of heatusing electricity or gas via line 636. Cool air is drawn from thestructure via return air duct 646 through fan 638 and is heated heatingcoils or elements 642. The heated air flows back into the structure atone or more locations via supply air duct system 652 and supply airgrills such as grill 650. In cooling an outside compressor 630 passesgas such a freon through a set of heat exchanger coils to cool the gas.The gas then goes to the cooling coils 634 in the air handlers 640 whereit expands, cools and cools the air being circulated through thestructure via fan 638.

The system is controlled by control electronics 612 that communicateswith a thermostat 610. According to some embodiments, the thermostat 610includes some or all of the occupancy prediction and/or detectionfunctionality described with respect to FIG. 1.

According to some embodiments, the occupancy predictions and/ordetections made by the techniques described herein may be used inprofiling the behavior of a structure for use by a control system of anHVAC system installed in the structure. For further details, seeco-pending U.S. patent application Ser. No. 12/881,463, by Fadell et.al., filed Sep. 14, 2010, which is incorporated herein by reference.

According to some embodiments, the device knows where it is, throughuser or admin setup or by GPS. For example, if an address is known viaGPS or by data entry, the device looks up the address and determines,for example, that the structure is a store, office, single-familydwelling, or apartment/condominium.

FIG. 7 is a schematic of a processing system used to predict and/ordetect occupancy of an enclosure, according to some embodiments. Inparticular, processing system 750 is used to perform much of thefunctionality described with respect to FIG. 1, as well as processingwithin device 510 described with respect to FIG. 5. Processing system750 includes one or more central processing units 744, storage system742, communications and input/output modules 740, a user display 746 anda user input system 748.

Although the foregoing has been described in some detail for purposes ofclarity, it will be apparent that certain changes and modifications maybe made without departing from the principles thereof. It should benoted that there are many alternative ways of implementing both theprocesses and apparatuses described herein. Accordingly, the presentembodiments are to be considered as illustrative and not restrictive,and the inventive body of work is not to be limited to the details givenherein, which may be modified within the scope and equivalents of theappended claims.

What is claimed is:
 1. An environmental control system for predictingoccupancy of an enclosure comprising: a memory storing a model ofoccupancy patterns comprising a plurality of coefficients; an occupancysensor configured to detect occupancy within the enclosure; a wirelesscommunication port that receives a location of a cellular phoneassociated with one of more of the occupants of the enclosure; one ormore processors programmed to: pre-seed the plurality of coefficientsfor the model of occupancy patterns with values that are selected basedon a geographic location of the enclosure and information describing aninitial occupant schedule received through a user interface; initiate alearning phase for the model of occupancy patterns after installation ofthe control system in the enclosure; combine information from theoccupancy sensor with the location of the cellular phone to generate anoccupancy status for the enclosure; generate a future occupancy patternof the enclosure, the future occupancy pattern being based at least inpart on the model of occupancy patterns and the occupancy status for theenclosure; refine the values for the plurality of coefficients using theoccupancy status for the enclosure during the learning phase; andcontrol an environmental condition of the enclosure based on the futureoccupancy pattern after an end of the learning phase.
 2. The system ofclaim 1 wherein the model is an a priori stochastic model of humanoccupancy.
 3. The system of claim 2 wherein the a priori stochasticmodel is a comfort and spatial occupancy model that includes one or morestatistical profiles.
 4. The system of claim 2 wherein the a prioristochastic model includes behavior modeling of activity, itinerary,and/or thermal behavior.
 5. The system of claim 1 wherein the model isbased at least in part on information selected from the group consistingof: a type of the enclosure, geometrical data about the enclosure,structural data about the enclosure, geographic location of theenclosure, an expected type of occupant of the enclosure, an expectednumber of occupants of the enclosure, the relational attributes of theoccupants of the enclosure, seasons of the year, days of the week, typesof day, and times of day.
 6. The system of claim 1 wherein the occupancysensor is selected from a group consisting of: motion detector,powerline sensor, network traffic monitor, radio traffic monitor,microphone, infrared sensor, accelerometer, ultrasonic sensor, pressuresensor, smart utility meter, and light sensor.
 7. The system of claim 1further comprising a second occupancy sensor, wherein the one or moreprocessors are programmed to generate the future occupancy pattern ofthe enclosure based at least in part on the model, the occupancy status,and the second sensor.
 8. A method for predicting occupancy of anenclosure comprising: accessing, by an environmental control system, amodel of occupancy patterns comprising a plurality of coefficients;receiving, by the environmental control system, occupancy data from anoccupancy sensor configured to detect occupancy within the enclosure;receiving, by the environmental control system, a location of a cellularphone associated with one of more of the occupants of the enclosure;pre-seeding, by the environmental control system, the plurality ofcoefficients for the model of occupancy patterns with values that areselected based on a geographic location of the enclosure and informationdescribing an initial occupant schedule received through a userinterface; initiating, by the environmental control system, a learningphase for the model of occupancy patterns after installation of thecontrol system in the enclosure; combining, by the environmental controlsystem, the occupancy data from the occupancy sensor with the locationof the cellular phone to generate an occupancy status for the enclosure;generating, by the environmental control system, a future occupancypattern of the enclosure based at least in part on the model ofoccupancy patterns and the occupancy status for the enclosure; refining,by the environmental control system, the values for the plurality ofcoefficients using the occupancy status for the enclosure during thelearning phase; and controlling, by the environmental control system, anenvironmental condition of the enclosure based on the future occupancypattern after an end of the learning phase.
 9. The method of claim 8further comprising receiving user inputted data, wherein the futureoccupancy pattern of the enclosure is generated further based in part onthe user inputted data.
 10. The method of claim 9 wherein the userinputted data includes occupancy information directly inputted by anoccupant of the enclosure and/or calendar information.
 11. The method ofclaim 9 further comprising detecting periodicities in the user inputteddata, wherein the future occupancy pattern of the enclosure is generatedfurther based in part on the detected periodicities in the user inputteddata.
 12. The method of claim 8 further comprising: comparing the futureoccupancy pattern of the enclosure with the occupancy data from theoccupancy sensor; and updating the future occupancy pattern based atleast in part on the result of the comparison.
 13. The method of claim 8wherein the occupancy sensor is one of a plurality of sensors arrangedat different sub-regions of the enclosure, receiving occupancy data fromthe occupancy sensor includes receiving occupancy data from theplurality of sensors, and generating the future occupancy pattern of theenclosure includes predicting future occupancy of the enclosure based atleast in part on the occupancy data received from the plurality ofsensors.
 14. The method of claim 8 wherein the future occupancy patternis based at least in part on a maximum-likelihood approach.
 15. Atangible non-transitory computer-readable storage medium havinginstructions stored thereon that, when executed by a computing device,cause the computing device to perform operations comprising: accessing amodel of occupancy patterns comprising a plurality of coefficients;receiving occupancy data from an occupancy sensor configured to detectoccupancy within the enclosure; receiving a location of a cellular phoneassociated with one of more of the occupants of the enclosure;pre-seeding the plurality of coefficients for the model of occupancypatterns with values that are selected based on a geographic location ofthe enclosure and information describing an initial occupant schedulereceived through a user interface; initiating a learning phase for themodel of occupancy patterns after installation of the control system inthe enclosure; combining the occupancy data from the occupancy sensorwith the location of the cellular phone to generate an occupancy statusfor the enclosure; generating a future occupancy pattern of theenclosure based at least in part on the model of occupancy patterns andthe occupancy status for the enclosure; refining the values for theplurality of coefficients using the occupancy status for the enclosureduring the learning phase; and controlling an environmental condition ofthe enclosure based on the future occupancy pattern after an end of thelearning phase.
 16. The storage medium of claim 15 wherein the model isbased at least in part on an occupant type.
 17. The storage medium ofclaim 16 wherein the occupant type depends on one or more occupantattributes selected from a group consisting of: age, school enrollmentstatus, marital status, relationships status with other occupants, andretirement status.
 18. The storage medium of claim 16 wherein theoccupant type is selected from a group consisting of: preschoolchildren, school-age children, seniors, retirees, working-age adults,non-coupled adults, vacationers, office workers, and retail storeoccupants.
 19. The storage medium of claim 15 wherein the model ofoccupancy patterns includes one or more types of models selected from agroup consisting of: Bayesian Network, Hidden Markov Model, HiddenSemi-Markov Model, variant of Markov model, and Partially ObservableMarkov Decision Process.
 20. The storage medium of claim 15 wherein thefuture occupancy pattern is used in one or more systems of a typeselected from a group consisting of: HVAC system, hot water heating,home automation, home security, lighting management, and charging ofrechargeable batteries.